Spaces:
Sleeping
Sleeping
""" CBAM (sort-of) Attention | |
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521 | |
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on | |
some tasks, especially fine-grained it seems. I may end up removing this impl. | |
Hacked together by / Copyright 2020 Ross Wightman | |
""" | |
import torch | |
from torch import nn as nn | |
import torch.nn.functional as F | |
from .conv_bn_act import ConvBnAct | |
class ChannelAttn(nn.Module): | |
""" Original CBAM channel attention module, currently avg + max pool variant only. | |
""" | |
def __init__(self, channels, reduction=16, act_layer=nn.ReLU): | |
super(ChannelAttn, self).__init__() | |
self.fc1 = nn.Conv2d(channels, channels // reduction, 1, bias=False) | |
self.act = act_layer(inplace=True) | |
self.fc2 = nn.Conv2d(channels // reduction, channels, 1, bias=False) | |
def forward(self, x): | |
x_avg = x.mean((2, 3), keepdim=True) | |
x_max = F.adaptive_max_pool2d(x, 1) | |
x_avg = self.fc2(self.act(self.fc1(x_avg))) | |
x_max = self.fc2(self.act(self.fc1(x_max))) | |
x_attn = x_avg + x_max | |
return x * x_attn.sigmoid() | |
class LightChannelAttn(ChannelAttn): | |
"""An experimental 'lightweight' that sums avg + max pool first | |
""" | |
def __init__(self, channels, reduction=16): | |
super(LightChannelAttn, self).__init__(channels, reduction) | |
def forward(self, x): | |
x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * F.adaptive_max_pool2d(x, 1) | |
x_attn = self.fc2(self.act(self.fc1(x_pool))) | |
return x * x_attn.sigmoid() | |
class SpatialAttn(nn.Module): | |
""" Original CBAM spatial attention module | |
""" | |
def __init__(self, kernel_size=7): | |
super(SpatialAttn, self).__init__() | |
self.conv = ConvBnAct(2, 1, kernel_size, act_layer=None) | |
def forward(self, x): | |
x_avg = torch.mean(x, dim=1, keepdim=True) | |
x_max = torch.max(x, dim=1, keepdim=True)[0] | |
x_attn = torch.cat([x_avg, x_max], dim=1) | |
x_attn = self.conv(x_attn) | |
return x * x_attn.sigmoid() | |
class LightSpatialAttn(nn.Module): | |
"""An experimental 'lightweight' variant that sums avg_pool and max_pool results. | |
""" | |
def __init__(self, kernel_size=7): | |
super(LightSpatialAttn, self).__init__() | |
self.conv = ConvBnAct(1, 1, kernel_size, act_layer=None) | |
def forward(self, x): | |
x_avg = torch.mean(x, dim=1, keepdim=True) | |
x_max = torch.max(x, dim=1, keepdim=True)[0] | |
x_attn = 0.5 * x_avg + 0.5 * x_max | |
x_attn = self.conv(x_attn) | |
return x * x_attn.sigmoid() | |
class CbamModule(nn.Module): | |
def __init__(self, channels, spatial_kernel_size=7): | |
super(CbamModule, self).__init__() | |
self.channel = ChannelAttn(channels) | |
self.spatial = SpatialAttn(spatial_kernel_size) | |
def forward(self, x): | |
x = self.channel(x) | |
x = self.spatial(x) | |
return x | |
class LightCbamModule(nn.Module): | |
def __init__(self, channels, spatial_kernel_size=7): | |
super(LightCbamModule, self).__init__() | |
self.channel = LightChannelAttn(channels) | |
self.spatial = LightSpatialAttn(spatial_kernel_size) | |
def forward(self, x): | |
x = self.channel(x) | |
x = self.spatial(x) | |
return x | |